The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of their ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision.
We are hiring an Economist on the team to develop the next generation of incrementality measurement products, capturing the effect of advertising in driving sales as well as the effects of measurement tools on advertiser engagement with Amazon. As an Economist on the team, you will lead the design, implementation, and validation of large-scale causal inference methodologies to capture these properties. You will communicate your results with science and business leaders, and partner with other scientists and engineers to carry solutions into production.
Key job responsibilities Leverage deep expertise in causal inference to develop robust, causally grounded ads measurement solutions Disambiguate problems to propose clear evaluation frameworks and success criteria Work autonomously and write high quality technical documents Partner closely with other scientists to deliver large, multi-faceted technical projects Share and publish works with the broader scientific community through meetings and conferences Communicate clearly to both technical and non-technical audiences and leaders Contribute new ideas that shape the direction of the team's work Mentor more junior scientists and participate in the hiring process
Basic Qualifications
- PhD in economics or equivalent
Preferred Qualifications
- 2+ years of industry, consulting, government, or academic research experience
- Experience in data mining (SQL, ETL, data warehouse, etc.) and using databases in a business environment with large-scale, complex datasets
- Experience in implementing modern machine-learning methods (e.g., boosted regression trees, random forests, neural networks)
- Experience in any of the following areas of applied economics (multiple areas preferred): reduced-form causal analysis, predictive modeling and causal ML algorithms, forecasting, hazard models, health and insurance economics, education economics, labor economics, and behavioral economics, designing and administering large-scale social science survey
- Knowledge of and proficiency in the use of Python scripting language
- Experience in the advertising industry or closely related problems.
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